Engineering economic analysis of the quick-germ/quick-fiber modified dry grind ethanol fractionation process

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ENGINEERING ECONOMIC ANALYSIS OF THE QUICK-GERM / QUICK-FIBER MODIFIED DRY GRIND ETHANOL FRACTIONATION PROCESS

BY TAO LIN

THESIS

Submitted in partial fulfillment of the requirements

for the degree of Master of Science in Agricultural and Biological Engineering in the Graduate College of the

University of Illinois at Urbana-Champaign, 2010

Urbana, Illinois Adviser:

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ABSTRACT

It has been widely debated whether producing ethanol from corn is sustainable in the long term. Environmentally, the major concern is that producing ethanol from corn involves intensive water and energy consumption. Economically, recent fluctuations in petroleum, ethanol, and corn prices have driven several large producers of ethanol into bankruptcy. The ethanol industry is vulnerable to periods of economic weakness because its product value varies with oil prices but its raw material (corn) varies with food prices.

To improve the economic sustainability of corn-to-ethanol production, several modified dry grind processes had been developed at the lab scale. The Quick-germ / Quick-fiber (QQ) process is one of them. However, there has been no analysis of the QQ process that provides detailed information related to the energy, water, and economic performance at a commercial scale. To determine the both environmental and economic performance, a process simulation model was developed on the SuperPro Designer® platform to simulate the QQ process, and compared to the conventional dry grind model.

Results indicate that germ and fiber recovery as done in the QQ process improves the process capacity of a conventional dry grind ethanol facility by approximately 24%. Because of germ and fiber recovery at the front end, the ethanol concentration has been increased to 15% (w/w) as compared to 10.9 (w/w) in the conventional dry grind process. The QQ process reduces the energy and water consumption by 32% and 17.8%, respectively.

The QQ process produces more value-added coproducts, including corn germ, corn fiber, and a modified distillers dried grains with solubles (DDGS), but has a lower ethanol yield rate due to some starch losses to the recovered germ and fiber fractions at the front end.

A detailed cost and benefit analysis of the QQ process, based on the market prices in April 2009, shows that despite its higher capital investment costs, the QQ process reduces the payback period to 6.5 years, compared to 9.2 years for the conventional dry grind process. Increased ethanol production, more value-added coproducts, as well as significant reduced utility costs are three major contributors to improve the economic performance of the QQ process. This work lays the foundation for the similar studies on the sustainability performance for other modified dry grind ethanol processes.

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ACKNOWLEDGMENTS

The fulfillment of this research project would not have been possible without the support of many people. Many thanks to my adviser, Dr. Luis F. Rodriguez, not only for his support in this project, but also for his constructive advice for setting my long-term objective. I would like to give special thanks to Dr. Steven R. Eckhoff for his assistance and guidance in this project. I would like to thank my other committee members, Dr. K.C. Ting and Dr. Madhu Khanna, for their guidance and support. In addition, I would like to thank Glen Menezes, Ryan P. Goss, and Haibei Jiang for their help during last two more years. I also thank the C-FAR organization for providing the funds for my graduate study as well as the project. Finally, thanks to my wife, Lijun Xia, and my parents, for their love and continuous encouragements during this long process.

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CHAPTER 1: INTRODUCTION

Renewable energy offers an opportunity to put our civilization on more sustainable ground, and it also offers countries around the world an opportunity to achieve energy

independence and can spur economic development. Biofuels are one of many renewable energy technologies. Although biofuels collectively offer many promising alternatives, ethanol

constitutes 99% of all biofuels in the United States (Farrell et al., 2006). The production of ethanol has increased rapidly from 227 million liters (60 million gallons) in the mid 1970s to 34 billion liters (9 billion gallons) in 2008 (RFA, 2009). Currently, corn is the primary feedstock source providing ethanol in the U.S., and the recent ethanol plant expansions in the industry are mainly based on the dry grind process.

It has been widely debated whether producing ethanol from corn is sustainable in the long term. Environmentally, the major concern is that producing ethanol from corn involves intensive water and energy consumption. Economically, recent fluctuations in petroleum, ethanol, and corn prices have driven several large producers of ethanol into bankruptcy. On the food supply issues, significant increase of ethanol production increases the demand of corn proportionally, driving the corn price to reach historically high levels. The increased amount of corn used for biofuel production may reduce the supply for food and animal feed production and decrease corn exports to the developing world. The ethanol industry is vulnerable to periods of economic weakness because its product value varies with oil prices but its raw material varies with food prices. When corn prices are high and ethanol prices are low, the dry grind processors lose money rapidly. Wet milling is a more stable method of ethanol production because of its coproduct values. When corn prices are high, coproduct value increases to offset the lower ethanol prices. However, when ethanol prices are strong, the dry grind processors do not have the capital requirement of wet milling and the coproduct value is not important to economic

performance. To stabilize ethanol production, dry grind ethanol processors need to develop their coproducts in order to get through the times when ethanol prices are low and corn prices are high.

Despite all these challenges, 136 billion liters (36 billion gallons) of biofuels are

projected to be produced in the US by 2022 (EPA, 2009). Aiming to achieve this ambitious goal, our long-term research objective is to develop biofuels production techniques that provide

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sustainable alternative energy sources that contribute to energy independence and spurring our rural economic development, without harming the supply for human food and animal feeds.

Corn-based ethanol remains the most viable biofuel available on the market today, and it will continue to be an important renewable fuel source in the near future. EPA proposed that corn-based ethanol is projected to produce 57 billion liters (15 billion gallons) by 2022. It seems imperative for the industry to find a better approach to making the corn-to-ethanol process sustainable, in order to facilitate migration to other bioenergy feedstocks in the future. There is a need to evaluate the sustainability of different corn-to-ethanol process technologies in order to improve the economic viability as well as reduce the environmental impacts of the biofuels industry.

There are three corn-processing techniques commercially in use today: dry grind, dry milling, and wet milling. Of these three techniques, dry grind and wet milling are processes used for fuel ethanol production from corn (Singh and Eckhoff, 1997), while the dry milling process is not used for ethanol production. Dry milling is primarily a physical separation process of corn components to produce the products including but not limited to corn grits, corn meals, and corn flours (Singh et al., 2001).

The current ethanol plant expansion in the industry is mainly based on the dry grind process. The conventional dry grind process is designed to ferment as much of the corn kernel as possible, to produce ethanol, distillers dried grains with solubles (DDGS) and carbon

dioxide. Given such a relatively simple process, with few options, dry grind ethanol plants is vulnerable to market fluctuations. Due to its high fiber content, DDGS, the only marketable coproduct from the dry grind process, can be only sold for ruminant animal diets. Expansion of the dry grind ethanol production will increase the supply of DDGS proportionately, but the current low market value of DDGS and its limited utilization will result in even lower prices.

In response to this need, several modified dry grind processes have been developed to improve the profitability of ethanol production, and significant improvements have been observed on the processing efficiency and the nutritional characteristics of coproducts at the laboratory scale (Singh et al., 2005). Quick-germ / Quick-fiber (QQ) process (Singh and

Eckhoff, 1996; Singh et al., 1999) is one of these modified dry grind processes, which uses part of the wet milling process (the germ and fiber recovery system) with the dry grind process. This process has three key advantages: 1) Recovered fiber and germ can be further processed to

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generate value-added coproducts such as germ oil, corn fiber oil and corn fiber gum. 2) Removal of germ and fiber can increase the protein content of the DDGS, making this coproduct suitable as a feed for non-ruminant animals such as swine and poultry. 3) Separating the nonfermentable fractions before fermentation will improve the process efficiency by 14% (Singh et al., 1999, Singh et al., 2005). Recently, Li et al. (2010) and Rodriguez et al. (2010) developed a detailed engineering economic spreadsheet model that showed the QQ process improves the economic viability of the dry grind process at the commercial scale.

Although economically viable, there has been no analysis that details the energy and water consumption of the QQ process and compares the process sustainability to the dry grind process. Therefore, the objective of the present study was to identify and compare the energy consumption, water usage, and economic performance of the QQ process and the conventional dry grind process. The USDA developed a simulation model for an ethanol facility using

conventional dry grind technology, and the results proved that process simulation modeling is an effective approach to predict the actual industrial scale operating performance (Kwiatkowski et al., 2006). Thus, the current rationale is that simulation modeling would be an effective approach to further consider the QQ process. It is expected that at the completion of this project, by

comparing to the energy and water demands, as well as the economic performance of the conventional dry grind process, the QQ simulation model will provide decision support for the adopting the QQ process technology at the industrial scale.

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CHAPTER 2: RESEARCH OBJECTIVES

The objective of this research is to provide decision support information relating to the economic and environmental sustainability performance of the QQ and the conventional dry grind ethanol processes. To achieve this research objective, the following specific goals are proposed:

1. Develop a simulation model on the SuperPro Designer® platform to simulate an ethanol facility using the QQ process technology and compare it with the USDA’s conventional dry grind process model.

2. Quantify and compare the energy and water demands of the QQ and the conventional dry grind processes.

3. Identify and assess the quantity and quality of products of the QQ and the conventional dry grind processes.

4. Evaluate the economic performance of the QQ and the conventional dry grind processes in terms of the differences on the capital investment costs, operating costs, and revenues.

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CHAPTER 3: LITERATURE REVIEW

3.1 DIFFERENT CORN-TO-ETHANOL PROCESS TECHNOLOGIES In the corn based ethanol industry, dry grind and wet milling are the two major

processing technologies. The major difference between these two technologies is whether corn is steeped at the beginning and is further separated into different fractions before fermentation.

The conventional dry grind ethanol process is designed to ferment as much of the corn kernel as possible. Without steeping and separation system, the dry grind ethanol process demands less capital investment and generates higher ethanol yield than wet milling process. Because of this, most recent ethanol production expansions are based on the dry grind process. In this process, the whole corn kernel is first hammer-milled to achieve size reduction, and then goes through cooking, liquefaction, saccharification, fermentation and distillation. The mash in the fermentation tank mainly consists of starch, protein, germ, and fiber fractions. Among these fractions, starch is the only fermentable material. The other three fractions dilute the fermentable substrates and decrease the ethanol productivity of the plant as a result. Apart from ethanol, CO2

and DDGS are the products from the conventional dry grind process, although DDGS is the only marketable coproduct. The primary market is as a feed for ruminant animals, because of its high fiber content. Due to the low value of DDGS, the conventional dry grind process is highly dependent on ethanol sales. Several economic failures of this process have occurred in the recent years due to the fluctuation of corn and ethanol prices. Some large ethanol producers even filed for bankruptcy in 2008 due to the significant fluctuation of corn prices (Wall Street Journal, 2009).

Wet milling is a complex process that can be divided into five sections: steeping, germ recovery, fiber recovery, protein recovery, and starch washing and processing. Each of these sections is unique but all sections are interconnected with the flow of process water. Steeping is the heart of the wet milling process as it softens the corn kernel to facilitate the following fractionation works. Ethanol, gluten meal, gluten feed, and crude corn oil are major products from wet milling ethanol process.

In order to improve the sustainability of corn based ethanol production, several modified dry grind processes have been developed at the lab scale in the last decade (Johnson and Singh, 2004; Singh and Eckhoff, 1996; Singh et al., 1999; Wahjudi et al., 2000). Most of those works

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incorporated prefractionation technology to separate nonfermentable materials at the front end, improving the fermentation operation efficiency. In addition, these recovered materials can be processed to produce more value-added coproducts.

Singh and Eckhoff (1996) proposed changes to the dry grind ethanol process by

recovering the germ before grinding the remaining material, which is called the ―Quick Germ‖ process. By using a soak time of 12 hr and soak temperature of 59°C, the germ can be recovered at levels comparable to those derived from the wet milling process (Singh and Eckhoff, 1996). Singh and Eckhoff (1997) reported that the DDGS produced in the Quick Germ process would be lower in fat and protein content.

The Quick Germ process has the potential to increase the coproduct credits, but the fermentability of the remaining corn slurry after germ recovery had not been tested. For this purpose, Taylor et al. (2001) conducted a research to measure the fermentation rate and yield of the Quick Germ process. The results showed that the concentration of suspended solids decreases significantly in the fermentation tank, which would reduce the operating costs.

Singh et al. (1999) proposed they have been able to recover corn coarse fiber with a process similar to the germ recovery. This process has a potential to produce more coproducts such as corn fiber gum (CFG) and corn fiber oil. The results showed that CFG yields in the quick fiber samples were comparable to that from the wet-milled coarse fiber samples.

In 1999, a new modified process, called Quick-germ / Quick-fiber (QQ), was further developed (Singh et al., 1999; Wahjudi et al., 2000). The QQ process allows the removal of germ and pericarp fiber as coproducts at the beginning of the dry grind corn process. This process has three key advantages: 1) Recovered fiber and germ can be further processed to generate valued coproducts such as germ oil, corn fiber oil and corn fiber gum. 2) Removal of germ and fiber can increase the protein content of the DDGS, making this coproduct suitable as a feed for non-ruminant animals such as swine and poultry. 3) Separating the nonfermentable fractions before fermentation will improve the process efficiency by 14% (Singh et al., 1999, Singh et al., 2005).

Another process modification called enzymatic milling (E-Mill) has been recently developed (Johnson and Singh, 2004). In addition to recovering germ and pericarp fiber, E-Mill further allows recovery of endosperm fiber as a valuable coproduct. Singh et al. (2005) reported that higher fermentation rate and higher ethanol concentration were obtained with the E-Mill

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process compared to the conventional dry grind process. Moreover, DDGS that is recovered from the E-Mill process could be used as a non-ruminant feed.

The results of these laboratory investigations showed that these modified dry grind ethanol processes have several advantages: 1) recovery of more value-added coproducts, 2) an increase of final ethanol concentration, and 3) an improvement of the nutritional characteristics of DDGS. However, little work has been conducted on the sustainability analysis of those modified dry grind processes at the industrial scale. Because of the added separation system, those modified dry grind processes might demand higher capital investment costs and possibly increase the water and energy demands. A comprehensive understanding of both economic and environmental performance is preferred to consider the sustainability performance of these novel technologies.

3.2 SCALE UP ANALYSIS

To develop a sustainable biofuel production technology, it is important to understand not only the product quality but also its energy and water demands to identify whether it is

environmentally friendly as well as economically viable. Based on the previous laboratory research results, our hypothesis is that modified dry grind ethanol processes will have inherent benefits on both environmental and economic aspects at the industrial scale. For the scale up analysis, several models have been developed to analyze the cost and benefits of the

conventional dry grind ethanol process, and the results demonstrated that modeling analysis is an effective tool to evaluate the actual processing performance (Dale and Tyner, 2006;

Kwiatkowski et al., 2006; Li et al., 2010; Rodriguez et al., 2010; Tiffany and Eidman, 2003). To evaluate a modeling performance for a corn-to-ethanol process, it is critical to understand its operating performance. Economically, the ethanol and coproducts yield rate and coproducts compositions are the most important factors for a prospective ethanol investor. Environmentally, energy and water demands from the ethanol production process are the critical pieces of information, where there are significant concerns aroused considering whether it is sustainable to produce fuel via processing corn. In addition, easy accessibility by the public would be another important factor of the modeling analysis. Therefore, five criteria are used for the sustainability analysis of corn-to-ethanol process, including mass balance, compositional driven, water balance, energy balance, and user accessibility (Table 3.1).

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Table 3.1. Comparisons of previous scale-up models for various corn-to-ethanol processes.

Compositional Driven Mass Balance Energy Balance Water Balance User Accessibility Conventional Tiffany and Eidman (2003) √

Dale and Tyner

(2006) √ √ Li et al. (2010); Rodriguez et al. (2010) √ √ √ McAloon et al. (2000) √ √ √ √ Kwiatkowski et al. (2006) √ √ √ √ Modified Li et al. (2010); Rodriguez et al. (2010) √ √ √

The model by Tiffany and Eidman (TE model) is clear and easy to understand the economic factors associated with the performance of dry grind ethanol plants. However, the TE model is not mass balanced (Tiffany and Eidman, 2003). That is, the amount of ethanol and DDGS produced from each bushel of corn is chosen as an input based on the industrial survey, rather than calculated based on the efficiency of each unit process. Without a mass balance, the TE model cannot predict the composition of DDGS. Thus, it lacks the ability to evaluate those modified dry grind ethanol processes with additional coproducts. Moreover, though the

parameters in the TE model are well researched and have been confirmed with plant managers, results are very sensitive to the input values chosen by the user (Dale and Tyner, 2006).

The model by Dale and Tyner (DM model) is mass balanced and feed backward. It allows the user to enter several parameter values of the dry grind process, such as the plant capacity, the composition of corn, and the physical condition at each step, to determine the necessary hourly flow rates of inputs and outputs at each step throughout the process (Dale and Tyner, 2006). Then, the hourly flow rates are used to calculate the equipment size and the capital cost. The hourly flow rates can also be used to calculate the utility consumption, and to estimate the operating costs. Based on these principles, the DM model is useful to conduct sensitivity analysis of the dry grind process, particularly with respect to the plant capacity. However, the

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mass balance of the DM model is only limited to starch, not for other components, such as proteins and fiber. Thus, it requires significant work to modify the DM model to conduct the analysis of a modified dry grind process.

Both the TE and DM models are focused on the economic analysis, whereas the energy and water consumption rates are input parameters that based on the industrial survey data. As ethanol production is a complex process, consisting of more than 100 unit operations,

spreadsheet modeling would not be an effective tool to provide detail information of the energy and water consumption throughout the process. Owing to the development of the process control and administration system, nowadays the energy and water usage as well as capital investment cost in the industrial plant can be well predicted by the computer simulation. SuperPro Designer® and Aspen Plus® are two tools being used to simulate the dry grind process (McAloon et al., 2000; Kwiatkowski et al., 2006).

The McAloon model is composed of two parts: a processing simulation model and a spreadsheet analysis model (McAloon et al., 2000). The processing simulation model is implemented on the ASPEN Plus® platform to achieve the mass and energy balances, and the simulation results are then incorporated in the Microsoft Excel® spreadsheet to conduct the sensitivity analysis. Although it would be easy for users to conduct a sensitivity analysis of different feedstock prices, changes in the process model would be required to construct new modified ASPEN Plus® simulation models.

More recently, USDA developed a conventional dry grind process model to simulate a 40 million gallon capacity facility upon the SuperPro Designer® platform (Kwiatkowski et al., 2006). The results derived from this model, such as energy and water consumption as well as capital investment and operating cost, agree with the current industrial operating performance. The research showed that model simulation would be an effective approach to represent a modern ethanol facility. This conventional dry grind simulation model can be used as a baseline and would allow the users to develop and evaluate both the economic and environmental impact of the process modifications.

As for the modified dry grind ethanol process analysis, Li et al. (2010) reported that a new engineering economic spreadsheet model was developed to analyze different ethanol production methods. Unlike previous spreadsheet models, this model is mass balanced and compositionally driven. A mass balanced model is integrated in order to check of each

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component mass flow and to ensure the yield and composition of each product are realistic. Within this model, the compositions of coproducts and input feedstock rates are input parameters that can be determined by users. Different types of the modified dry grind ethanol process will result in different compositions of coproducts. By inputting the composition of those coproducts, it is possible for users to evaluate the economics of various modified dry grind ethanol

productions. However, this model also relies on several assumptions, particularly in the areas of energy and water consumption as well as the capital investment cost.

3.3 PREVIOUS STUDIES ON THE ENERGY DEMAND

Rapid growth in ethanol production has recently received considerable attention throughout the nation. Many studies analyzing the energy balance issue began to appear in the literature since early 1990’s (Keeney and Deluca, 1992; Shapouri et al., 2002; Pimentel, 2003). Those studies conducted a life cycle assessment of the energy consumption in corn based ethanol production by calculating its net energy value (NEV). NEV is defined as the energy content of ethanol minus the fossil energy used to produce ethanol (Shapouri et al., 2002). However, the findings of those life cycle assessments varied significantly.

Keeney and DeLuca (1992) reported a negative NEV, and the deficits of the results are less than 2.79 MJ per liter of ethanol (MJ/liter) (10,000 BTU/gal). Pimentel (2003) reported a significant energy deficit in the corn-to-ethanol production, with a net energy value loss of -6.17 MJ/liter (-22,119 BTU/gal). Put another way, the energy required to produce each gallon of ethanol is about 29% more than the energy content of each gallon ethanol. More recently, Pimentel and Patzek (2005) reported that there is still 28% energy loss in producing ethanol. However, in recent papers, most results showed that producing ethanol from corn can achieve a positive net energy value (Farrell et al., 2006; Shapouri et al., 2002; Shapouri et al., 2003; Shapouri and Gallagher, 2005; Wang et al., 1999).

This wide variation of energy consumption results from varied assumptions in terms of farm production, ethanol production technologies, and coproducts evaluation. Despite the detail in each of these papers, it may be difficult to understand why the disparity is so great. The energy consumption in the life cycle of the corn based ethanol production can be composed of three parts: farming, transportation, and ethanol production. For this analysis, we focus on the energy demand of ethanol production.

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11 3.3.1 Energy Sources for an Ethanol Plant

Thermal energy and electricity are the main types of energy used in the ethanol production. Thermal energy, in the form of steam and hot air, is used in liquefaction,

fermentation, and distillation. However, due to its lower efficiency, boiler steam is not always used for drying in a natural gas fired ethanol plant. Electricity is required in all stages of the ethanol production process to run motors, pumps, and other unit operations such as dewatering press and molecular sieve. Currently, most dry grind ethanol plants only generate steam onsite and purchase electricity from a utility.

Natural gas and coal are two sources typically used to generate steam. Each energy source requires a unique set of equipment to generate steam. For coal fired ethanol plants, the most common equipment type includes a fluidized-bed boiler energy system for steam

generation, and a steam fired dryer for DDGS drying. In contrast, for natural gas ethanol plants, a natural gas boiler is usually utilized to generate steam and a natural gas fueled direct-fired dryer for drying DDGS (Mueller and Cuttica, 2006).

Mueller and Cuttica (2006) reported a detailed energy and economic analysis between coal fired and natural gas fired ethanol plants. The report estimated that the thermal fuel use, not including electricity, in the coal fired system is 11.2 MJ/liter (40,256 BTU/gal) compared with 9.0 MJ/liter (32,330 BTU/gal) for the natural gas fired system. The report further illustrated the reasons why coal fired systems require higher thermal energy: 1) the boiler efficiency of the coal fired system is lower than that of the natural gas fired system, which are 78% and 80%,

respectively; 2) the steam fired dryer used at coal fired plants requires higher thermal energy compared to the direct fired dryers used at natural gas plants. The report also showed detailed energy flows for both natural gas and coal fired plants. However, this report did not provide any sources to verify whether the data regarding the energy consumption in each step of the process are reliable.

In 2007, the Renewable Fuels Association (RFA) conducted a survey of US ethanol production plants (Wu, 2008). A majority of dry grind ethanol facilities (86%) are fueled with natural gas. The remaining 14% are coal fired dry grind ethanol plants that supplement coal use with a range of natural gas (3-23%) as the process fuel (Wu, 2008).

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12 3.3.2 Energy Requirement in Negative NEV Papers

Pimentel (2003) reported that the energy balance of corn based ethanol production is negative, based on a personal communication with an industrial expert. In this analysis, 10.9 MJ of coal and 0.73 kWh of electricity are required to produce one liter of denatured ethanol In more recent works (Pimentel and Patzek, 2005; Pimentel et al., 2007), the data of steam power and electricity demand of the ethanol production is from the website of Illinois Corn Growers Association. According to those papers, the process requires 10.7 MJ of steam and 1.17 kWh of electricity to produce each liter of ethanol. This result indicated that the energy demand is even higher than that in his previous report. However, based on the updated information from Illinois Corn Growers Association (2008), the results showed that the ethanol production requires 10.7 MJ of steam and 0.4 kWh of electricity in average to produce each liter of ethanol. There is a non-trivial difference on the electricity consumption as compared to the data previously used by Pimentel. What is more, the source for the energy demand of the ethanol production at the website of Illinois Corn Growers Association is over a decade older. It is questionable whether those data represent the current energy demand of ethanol production.

3.3.3 Energy Requirement in Positive NEV Papers

In 2002, USDA conducted a survey to provide a complete picture of the dry grind ethanol industry. According to the survey (Shapouri and Gallagher, 2005), dry grind ethanol plants consume 0.31 kWh of electricity to produce one liter of ethanol, ranging from 0.16 to more than 0.53 kWh. On average, the surveyed ethanol plants utilize 9.7 MJ of thermal energy to produce each liter of ethanol, ranging from 7.2 to 15.1MJ. The average of total energy use in the ethanol production is 10.8 MJ/liter (38,861 BTU/gal).

USDA’s net energy balance of corn-to-ethanol life cycle analysis was published in 2002 and 2003 (Shapouri et al. 2002; Shapouri et al. 2003). For the estimations of the energy

consumption in the ethanol production, both papers are based on the USDA 2002 survey. On average, the dry grind ethanol plants used 0.29 kWh of electricity and over 10.0 MJ of thermal energy (HHV) to produce each liter of ethanol. However, these data are different from the original USDA’s survey, which reports 0.31 kWh of electricity and 9.7 MJ of thermal energy. The distinction of these two papers (Shapouri et al. 2002; 2003) is that they considered an energy loss in producing electricity and natural gas. Considering the generation efficiency, the dry grind

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ethanol plant consumed 13.6 MJ of primary energy to produce each liter of ethanol (Shapouri et al., 2002). However, there is no detailed description on the how these energy losses of electricity and natural gas were determined.

More recently, Shapouri and Gallagher (2005) reported that the energy output/input ratio is 1.67, indicating corn ethanol production is energy efficient. The energy consumption in the production is still based on the USDA 2002 survey. Although this paper estimated the thermal energy using a lower heating values (LHV) assumption, other data regarding the energy use in the ethanol production remains the same with previous papers. Still, there is no detailed information on the how to measure the energy losses in producing electricity and natural gas.

Farrell et al. (2006) constructed a model named EBAMM to evaluate six representative analyses of fuel ethanol. The results indicated that current corn ethanol technologies are much less petroleum-intensive than gasoline. However, this paper also points out several errors, omissions and inconsistencies in the construction of the EBAMM model, especially steam and electricity use in corn ethanol production is based on the data that is over a decade older.

Mueller (2010) reported that based on the 2008 national dry grind ethanol plant survey, the average energy consumption had been reduced to 7.18 MJ/liter of thermal energy and 0.195 kWh/liter.

3.3.4 Energy Demand Analysis

As described above, wide variations of the energy demand in the previous papers occur as a result of different data sources. The recent RFA industrial survey showed that significant improvements have been achieved to minimize the energy demand in the past several decades (Wu, 2008). The average energy demand, including electricity, of a dry grind plant was reduced from 11.1 MJ/liter (39,719 BTU/gallon) in the 2001 USDA survey to 8.7 MJ/liter (31,070 BTU/gallon) in the 2008 RFA survey, which is ranged from 4.9 to 12.3 MJ/liter (Wu, 2008). However, the industrial survey only provides the total energy consumption rate for ethanol production, but lacks the detailed operating performance. Considering the public criticisms that corn based ethanol production is energy intensive, it is important to understand the detailed energy demand by each unit operation, and thus providing the basis for the future optimization work to minimize energy demand.

Recently, a conventional dry grind ethanol process model, developed by USDA, demonstrated that simulation modeling is an effective tool to estimate the actual operating

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performance (Kwiatkowski et al., 2006). The results of energy demand derived from this model well represent the current industrial operating performance. Moreover, this simulation model quantifies the energy demand of each unit operation, which would facilitate the future process optimization work.

The QQ process was developed to improve the performance of corn based ethanol production (Singh et al., 1999; Wahjudi et al., 2000). Laboratory results of the QQ process demonstrate that the separation of nonfermentable materials before fermentation would improve the ethanol concentration. Therefore, our hypothesis is that this novel process would reduce the energy demand by a more efficient ethanol recovery.

However, since no existing plant has adopted the QQ process, industrial survey would not be utilized to support our hypothesis. Therefore, modeling simulation would be an approach to estimate the performance of this novel process. A new QQ process simulation model is built to quantify the energy demand of each unit operation in the QQ process. This model is further compared with the USDA model to evaluate the energy performance of these two dry grind ethanol processes.

3.4 PREVIOUS STUDIES ON THE WATER DEMAND

With the rapid growth in the ethanol industry, water availability and utilization are the key issues that must be addressed. In the life cycle of ethanol production, water is used in two major areas: farming and plant operation. Wu et al. (2009) claimed that irrigation water use for growing corn has substantial variation by state and region, ranging from 3 to 129 liters to produce each kilogram of corn. Irrigation water will dominate the total water usage in the life-cycle of ethanol production if the ethanol plant processes the corn from irrigated fields. In the life cycle analysis, the water demand for each gallon of ethanol production ranges from a net of 10-17 liters in non-irrigated corn production regions to 324 liters in irrigated corn production regions (Wu et al., 2009). However, although the water used in ethanol production is relatively small as compared to that used in the farming, significant public concerns of intensive water usage in ethanol production have been aroused with recent ethanol production growth. The major concern is that whether the increased water demand for ethanol production would adversely impact the adequate fresh water supply in the region near the ethanol facility. For this purpose, this analysis will focus on the water demand of ethanol production.

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Water plays an important role in ethanol production and is generally categorized as either process water or non-process water. Process water refers to water directly mixed with corn, whereas non-process water is used for heating and cooling in the process, including cooling water and steam water. In ethanol production, process water is required for soaking, forming a corn slurry, which facilitates pumping of materials throughout the process. For the process water, dryer vapor loss would be the major loss, whereas a small fraction of loss is due to water being incorporated in the final products. Since the operating temperature changes in the various unit operations, cooling water and steam are needed to provide the optimum operating environment. The majority of cooling systems utilize a recirculating non-contact system, where the cooling water loss occurs in the cooling tower via evaporation, drift, and blowdown.

Pimentel reported that 13 liters of water are required to produce one liter of ethanol (Pimentel, 2003; Pimentel et al., 2005). It is true that large amounts of water are required to make the process slurry in corn-to-ethanol plants, however, most plants can recycle a significant portion of their process water through a combination of centrifuges, evaporation and anaerobic digestion (Aden, 2007). With the help of the process water recycling, the water demand can be significantly reduced. Shapouri and Gallagher (2005) reported that ethanol plants used more than 15 liters of water to produce each liter of ethanol in the 1980. However, based on the USDA 2002 survey, the average water usage had been reduced to 4.7 liter of water per liter of ethanol (liter/liter), ranging from 1 to 11 liter/liter (Figure 3.1).

Figure 3.1. Average consumptive water usage in the existing dry grind ethanol plants (Data source: Shapouri and Gallagher, 2005; Keeney and Muller, 2006; Wu, 2008).

15 5.8 4.7 _4.2 3.45 0 2 4 6 8 10 12 14 16 1980 1998 2002 2005 2008 Wate r C o n su m p tion , lite ro f wat e r p e rl ite r o f e th an o l Year

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Keeney and Muller (2006)reported that average water use of ethanol plants located in Minnesota has declined from 5.8 liter/liter in 1998 to 4.2 liter/liter in 2005, indicating that the plants are achieving greater efficiency over time. This report also provided several

recommendations on how to reduce the water consumption in ethanol production: 1) use municipal wastewater as a source of water input; 2) locate the ethanol plant close to livestock facilities in order to sell the wet distillers grains without drying; 3) place a greater economic value on water to promote water conservation in the ethanol industry.

Recently, Wu (2008) reported that based on the latest ethanol plant survey, currently dry grind ethanol plants consume an average of 3.45 liters of fresh water to produce each liter of ethanol, ranging from 2.65 to 4.9 liter/liter. Newly built plants tend to require less water, because of the improved equipment and energy efficient design.

Typically, the water losses in the different ethanol plants vary with the evaporation rate in the cooling tower, and the percentage of water vapor captured in the DDGS dryer (Wu et al., 2009). Further, based on the USDA dry grind process model, assuming a temperature drop of 20˚F for the cooling tower, no recapture of dryer vapor loss, and a boiler makeup water rate of 5 percent, Wu et al. (2009) proposed a breakdown of water usage in the dry grind ethanol

production as shown in Figure 3.2. The result shows that cooling water and dryer vapor loss account for the major water loss in the corn-to-ethanol production.

Figure 3.2. The breakdown of water usage in the dry grind ethanol production (Wu et al., 2009).

Cooling Tower 53% DDGS 2% Dryer 42% Boiler 3%

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17 3.5 PREVIOUS STUDIES ON THE ECONOMIC ANALYSIS

Corn based ethanol production has increased exponentially in the last two decades from 227 million liters (60 million gallons) in the mid 1970s to 34 billion liters (9 billion gallons) in 2008 (Renewable Fuels Association, 2009). However, recent fluctuations in petroleum, ethanol, and corn prices have driven several large producers of ethanol into bankruptcy, mergers, and acquisition. Despite these economic challenges, corn based ethanol still remains the most viable biofuel available on the market today and is projected to produce 57 billion liters (15 billion gallons) by 2022 (EPA, 2009). Therefore, it is critical to understand what are the important factors affecting the economic performance of corn based ethanol production. Based on previous economic studies, there are four major factors associated with the economic performance of corn based ethanol production, including capital investment costs, operating costs, ethanol prices, and ethanol yield rate. These major factors will be considered below.

3.5.1 Capital Investment Costs

The capital investment is the total amount of money needed to supply the plants and manufacturing facilities plus the amount of money required as working capital for operation of the facilities (Peters et al., 2003). There are few papers discussing the sensitivity of profit with regard to capital investment costs mainly because the typical design of a standard plant has not changed much over the past few years. According to Eidman (2007), due to the high cost of stainless steel, copper and concrete, the fixed capital investment costs have increased to $0.4 per liter of output in 2006 from $0.26 in 2003 for a 120 MGY dry grind ethanol plant.

Gallagher et al. (2005) proposed that capital costs typically increase less than

proportionately with plant capacity in the dry grind ethanol industry. Therefore, larger plant capacity will have an advantage with respect to fixed capital investment costs. However, capital costs of an ethanol plant increases more rapidly than the average of all processing plants, where the power factors are 0.836 and 0.6, respectively (Gallagher et al., 2005). More specifically, Eidman (2007) estimated that the capital investment cost would be $0.33 per liter for a 151 MLY (40 MGY) dry grind ethanol plant, compared with $0.26 per liter for a 379 MLY (100 MGY) plant.

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18 3.5.2 Operating Costs

During recent years, the dry grind ethanol production has attained a significant

improvement in the processing efficiency. McAloon et al. (2000) reported that the production cost per liter had decreased to $0.23 in 2000 from $0.65 in 1978. Energy saving technologies and higher ethanol yield per bushel are the two factors reducing the production cost (McAloon et al., 2000). There are several other factors associated with the production cost discussed below.

The single greatest cost in the dry grind ethanol production is the feedstock corn cost, which accounts for more than half of the total production cost. Corn prices vary from year to year, and the variations have been especially drastic in recent years. McAloon et al. (2000) reported that when corn prices are $0.076 per kg ($1.94 per bushel), the feedstock cost accounts for $0.18 of each liter ethanol produced. The total production costs of the same facility are $0.23 per liter for a 25 MGY ethanol plant. Since DDGS prices follow corn prices, when analyzing the production costs, the feedstock cost of the dry grind process is often computed on a ―net cost‖ basis, which is offset by the revenue gained from the sales of DDGS. Wyman (1996) reported when corn is $0.12 per kg ($2.94 per bushel), the gross feedstock cost accounts $0.23 per anhydrous liter ($0.87 per anhydrous gallon), and the net feedstock cost is reduced to $0.12 per anhydrous liter ($0.45 per anhydrous gallon). Kwiatkowski et al. (2006) reported that the cost of producing ethanol increased from $0.24 to $0.36 per liter while the price of corn increased from $0.07 to $0.13 per kg; however, this analysis did not consider that DDGS price would fluctuate with changes in corn price. Eidman (2007) conducted a sensitivity analysis of corn price, and the results showed that if DDGS price remains $0.08 per kg ($71.43 per ton), every $0.04 per kg ($1 per bushel) increase of corn price would result in a $0.094 per liter ($0.356 per gallon) increase of the net ethanol production cost. However, the markets suggest the price of DDGS follows corn price. Assuming the price of DDGS increases at approximately 92% of the increase in the corn price, Tiffany and Eidman (2003) proposed the net ethanol production cost increases $0.07 per liter ($0.26 per gallon) at each $0.04 per kg ($1 per bushel) increase of corn price. More recently, Li et al. (2010) developed a model to estimate the price of DDGS based on corn price, protein and NDF content of DDGS, and this model will enhance the research on the net

production cost analysis.

Utility costs are another important factor associated with the operating costs. In a dry grind ethanol plant, energy is utilized in two forms, thermal energy and electric power. Thermal

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energy is of greater importance because it dominates the total energy consumption in ethanol production. Most of the previous studies assume natural gas is the source providing thermal energy. For example, Eidman (2007) proposed that a dry grind ethanol plant requires 8,983 BTU of natural gas to produce one liter of ethanol, hence a $1 change in the price of natural gas will result in a production cost change of $0.009 per liter ($0.034 per gallon). However, the amount of energy consumption in the ethanol production varies in the previous papers, as discussed in Chapter 3.3.

3.5.3 Ethanol Prices

Ethanol is the most valuable product in the dry grind process. Tiffany and Eidman (2003) claimed, for a 151 MLY (40 MGY) dry grind ethanol plant, an additional revenue of $480,000 per year can be returned by each $0.003 per liter ($0.01 per gallon) increase of the fuel ethanol price. Based on the energy content equivalence, Tyner and Taheripour (2007) claimed that the ethanol price should be 70% of the wholesale gasoline price. Despite its low energy density, ethanol has an additive value as a result of the higher oxygen and octane content. Historically, the ethanol price was higher than the wholesale price of gasoline by an average of $0.09 per liter ($0.35 per gallon) from 2002 to 2004, when the ethanol supplies could not fulfill the demand (Eidman, 2007). Industry representatives suggest the ethanol price should be $0.053 to $0.066 per liter ($0.20 to 0.25 per gallon) higher than wholesale price of gasoline when ethanol supplies meet the demand (Eidman, 2007).

Over in the medium term, the price of gasoline can be predicted from the price of crude oil. Tyner and Taheripour (2007) claimed an econometric relationship for gasoline prices where wholesale gasoline price ($/gal) = 0.1076 + 0.031270*crude oil price ($/barrel). Estimated from national data for the period from January 2000 to 2006, Eidman (2007) concluded that wholesale gasoline price ($/gal) = 0.037 + 0.030*crude oil price ($/barrel). Assuming the ethanol price is $0.05 per liter ($0.20 per gallon) higher than the wholesale price of gasoline, by using Eidman’s relationship equation, the price of ethanol should increase $0.08 per liter ($0.3 per gallon) as the price of crude oil increases $10 per barrel.

3.5.4 Ethanol Yield Rate

The amount of ethanol yield by each bushel of corn processed is the most important factor for plant managers. Currently, one kg of corn generates 0.41 liter of anhydrous ethanol

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(Tiffany and Eidman, 2003). Recently, many researchers have investigated the improvement of the ethanol yield efficiency. Developing high starch concentration corns and producing more effective enzymes and better yeast strains are some of the examples. Tiffany and Eidman (2003) proposed that as the ethanol yield rate increased by 0.015 liter per kg (0.1 gallon per bushel), a 151 MLY (40 MGY) ethanol plant would improve its profits by $801,550 per year.

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CHAPTER 4: METHODOLOGY

4.1 PROCESS MODEL DEVELOPMENT

As described above, the USDA SuperPro Designer® simulation model is a proven tool to predict actual operating performance for a modern ethanol facility using conventional dry grind ethanol process. Due to its promising laboratory results, the QQ process was selected for consideration in this analysis. A QQ process model was developed based on the information from the USDA model as well as laboratory experimental results. The process model was

developed in the SuperPro Designer® platform to evaluate its mass and energy balance. The user may now select a feedstock input rate and the model quantifies the volume, composition, and other physical characteristics of the process streams for each unit operation. In addition, the user may select the unit operation requirements, such as residence time, working temperature, and operation efficiency. This information becomes the basis for sizing the equipment, estimate the energy and water demands, and subsequently provide cost and benefit analysis for the QQ process.

Due to the germ and fiber removal at the front end, the QQ process improves its processing capacity relative to the conventional dry grind process. The conventional dry grind model developed by USDA is designed to process 1.1 million kg of corn per day (43,800 bu/day) (Kwiatkowski et al., 2006). To compare both processes consistently, a QQ process model is designed to process 1.3 million kg of corn per day (52,500 bu/day), without increasing the size of the equipment used in the conventional dry grind process. The QQ ethanol plant operates

24h/day and 350 days per year, with time set aside for maintenance and repairs.

The QQ process consists of six major sub-systems, including grain cleaning, germ and fiber recovery, liquefaction and saccharification, fermentation, distillation, and DDGS recovery. The simplified flow sheet is given in Figure 4.1. Each unit operation in the model is identified a number ID based on each one of the six sections and the type operation. For example, 103BC identifies the belt conveyer in the grain cleaning section (100’s for grain cleaning section), and 313HX represents the heat exchanger in the liquefaction and saccharification section (300’s for liquefaction and saccharification section). Table 4.1 gives an overview of some of the key unit operations in the QQ process model.

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Figure 4.1. Simplified flow sheet of the QQ process.

Saccharification Liquefaction Soaking Fermentation Centrifugation Dehydration Distillation CO2 Scrubber Evaporation DDGS CO2 Ethanol Fresh Water

Germ & Fiber Separator Germ and Fiber Clean Corn Grinding Jet Cook Cooler Separator Dryer Vent Whole Stillage Thin Stillage Wet Cake Dryer Vent Liquid Slurry Gas

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Table 4.1. Overview of selected QQ process equipment.

Unit ID Name Description

102 SL Corn storage 72 hr Residence time

104 U Grain cleaner 0.3% Removed as trash to downstream

201V Soaking tank 8 hr Residence time

214 PFF Dewatering press 0.46 kW/m2 Specific power

Output dryness: 50% moisture content

217 FBDR Fluid-bed dryer Natural gas direct fired: 0.06 kg/kg evaporated1 0.04 kW/(kg/h) Specific power

303 V Slurring mix tank 0.25 hr Residence time

306 HX Liquefaction heater 87.8 °C Exit temperature

307 V Liquefaction tank 0.9 hr Residence time

0.6 kW/m3 Specific power1

310 HX Jet cooker 110 °C Exit Temperature

318 V Saccharification tank 20 min Residence time

403 HX Cooler 32.3 °C Exit temperature

406 FR Fermentor 60 hr Residence time

408 V Degasser 0.05 hr Residence time

409 C Condenser 98.154% ethanol condensation ratio

1.2% CO2 condensation ratio

505 MS Molecular sieves 99.7% ethanol in outlet

14 kW operating power1 512 V Anhydrous ethanol day tank 100 hr Residence time 515 V Denaturant ethanol day tank 156 hr Residence time

604 SC Stillage centrifuge 30% (w/w) solids in underflow

609 E Thin stillage evaporator Four-effect evaporator

35% solids in syrup

615 D DDGS dryer Natural gas direct fired: 0.06 kg/kg evaporated1

616 TO Thermal oxidizer 234 kW heat duty1

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24 4.1.1 Corn Composition

An understanding of composition of corn is vital for conducting sustainability analysis of the QQ process. The corn kernel is a seed that has four easily separable parts: tip cap, pericarp, endosperm, and germ (Watson, 1987). The benefits of the QQ process are ultimately determined by the amount of the germ and fiber recovered at the front end, and how this recovery affects the following operations in terms of ethanol yield rate, utility demand, as well as the compositions of coproducts. Dry basis is usually used in the mass balance analysis because the amount of dry matter does not change in the system while the amount of water may. Assuming 14.5% moisture content, 1 bushel of corn (56 lb) will have 47.88 lb of dry material.

In the QQ process, the tip cap is always separated together with the pericarp. Thus, to simplify the model, corn kernel is divided into three parts: pericarp + tip cap, endosperm and germ. Starch, oil, neutral detergent fiber (NDF), protein, sugars, and ash are major constituents making up the corn kernel. To better illustrate the recovered components by the QQ process, the protein and NDF can be further divided based on their distribution in the each part of the corn kernel. Table 4.2 and 4.3 show the fractions as measured by lb/bu and percent dry basis (Unpublished data, Eckhoff).

Table 4.2. Compositional components in the different corn fractions (lb/bu).

Corn Endosperm Germ Pericarp+Tipcap

47.88 39.78 5.30 2.80

Protein 4.55 3.24 1.15 0.16

-Insoluble germ protein 0.45 - 0.45

-Insoluble nongerm protein 3.04 2.98 - 0.06

-Soluble protein 1.06 0.26 0.71 0.10 NDF 4.55 1.32 0.82 2.41 -Cellular fiber 1.32 1.32 - -Coarse fiber 2.41 - - 2.41 -Germ fiber 0.82 - 0.82 -Starch 34.71 34.12 0.42 0.17 Sugar 1.20 0.35 0.83 0.02 Oil 2.01 0.28 1.70 0.03 Other 0.34 0.31 0.01 0.02 Ash 0.53 0.11 0.54 0.02

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Table 4.3. Compositional components in the different corn fractions (percent db).

Corn Endosperm Germ Pericarp+Tipcap

47.88 39.78 5.30 2.80

Protein 9.50% 8.14% 21.73% 5.68%

-Insoluble germ protein 0.93% - 8.41%

-Insoluble nongerm protein 6.36% 7.50% - 2.16%

-Soluble protein 2.21% 0.64% 13.32% 3.51% NDF 9.50% 3.32% 15.46% 85.97% -Cellular fiber 2.76% 3.32% - -Coarse fiber 5.04% - - 85.97% -Germ fiber 1.71% - 15.46% -Starch 72.50% 85.78% 8.00% 5.94% Sugar 2.50% 0.87% 15.66% 0.77% Oil 4.20% 0.71% 32.12% 1.00% Other 0.70% 0.78% 0.19% 0.71% Ash 1.10% 0.28% 10.16% 0.72%

Fermentable components in the corn kernel are the key for ethanol production. Starch and sugars are the two primary fermentable components. Due to the limited amount of sugars in the corn kernel, starch is the most valuable resource in the corn kernel for an ethanol facility. Starch accounts for 72.5% of the total dry matter of the corn kernel and is primarily located in the endosperm.

The fiber fraction can be divided into three distinct parts. The fiber found in the germ meal, germ fiber, is not separable and accounts for approximately 18% of the fiber in the kernel. Approximately 53% of the corn fiber is classified as coarse fiber in our work, located in the pericarp and tipcap, with the remaining 29% as endosperm cellular fiber.

Traditionally, corn proteins are classified into four types based on their solubility: albumins, globulins, zein, and glutelins (Watson, 1987). To simplify our model, protein is classified based on the solubility as well as on the location, including insoluble germ protein, insoluble nongerm protein, and soluble protein. Insoluble germ protein is limited in the germ fraction, while insoluble nongerm protein is mainly from the endosperm with a small fraction from the pericarp and tipcap. Approximately 70% of soluble protein is from the germ fraction.

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26 4.1.2 Grain Cleaning

Corn is shipped in bulk to the ethanol plants by truck, hopper car, and barge. Using a belt conveyer, corn is placed in a storage bin (102 SL) sized to hold corn for three days of operation. Prior to soaking, a grain cleaner (104 U), represented by the two-way component splitter in this model, is applied to separate foreign materials and broken kernels, which account for 0.3% of total flow rate as debris in the bottom product (Kwiatkowski et al., 2006). Those foreign materials and broken kernels are removed to prevent unnecessary viscosity increase during the process and maintaining the quality of the products.

4.1.3 Germ and Fiber Recovery

The germ and fiber recovery sub-system is the key innovation in the QQ process, which is adopted from the wet milling systems (Figure 4.2). Soaking is the critical step in the QQ process, which affects the consequent germ and fiber recovery performance. The soaking step increases the moisture content of the grain, softens the kernels to facilitate the germ and fiber separation, and preserves the integrity of the germ during grinding. The soaking tank (201 V) in the model is sized for 8 hr residence time to make sure the corn has been well soaked. The moisture content of the slurry will increase from 14.5 to 57.4% during soaking. To minimize the water and energy use, the soaking water in this model is sourced by recycling three streams consisting of the downstream of the CO2 scrubber, the downstream of the stripper distillation

tank, and the condensed water vapor from the thin stillage evaporator. The temperature of this soaking water input is approximately 68˚C, which provides the necessary heat to meet the soaking requirements at 55˚C. Therefore, no external heaters are required to equip with the soaking tank. The design of this soaking water input reduces both water and heating demands.

After soaking, the corn slurry is then sent to the coarse grinding mills (ID: 202 GR) to lightly break up the corn kernel, and thus germ and fiber can be separated from the slurry by hydrocyclones. The separation system, based on the density difference, consists of two stages of hydrocyclones and three stages of wash screens. In this model, the wash screen is represented by a two-way component splitter. The diluted slurry from the mills is pumped to hydrocyclones, where the lighter parts, germ and fiber, are floated off the top. The purity of the germ and fiber fraction is determined by the top flow ratio of the first hydrocyclone (Table 2.5). All of the separated germ and fiber parts leave in the top flow of the first hydrocyclone and are routed to

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the washing system; a starchy slurry leaves in the underflow and proceeds to the second hydrocyclone. The second hydrocyclone is designed to minimize the germ and fiber loss in the starchy slurry by recycling its top flow back to the grinding tank. The top flow ratio of the second hydrocyclone determines the amount of germ and fiber that is recycled to the grind tank (Table 4.4). The extent of the separation is derived from the laboratory experimental results (Li et al., 2010), and it can be easily adjusted based on other experimental data.

Figure 4.2. The germ and fiber recovery sub-system in the QQ Process.

The washing system consists of three gravity screens, aiming to wash the loose starch and protein from the germ and fiber parts. Water is acquired from CO2 scrubber downstream and is

recycled to the last stage of washing. The washing water runs in a counter current fashion and finally leaves in the underflow of the first screen with the free starch and protein. The detailed information of separation ratios of the wash screens are shown in Table 4.4, and are based on industrial data and a mass balance of the process.

After washing, the germ and fiber are then dewatered in the screw press (ID: 214 PFF), which is represented by a plate and frame filter in this model, to an average of 50% moisture content. Germ and fiber are further dried and separated via a fluid bed dryer (ID: 217 FBDR) to 3% and 10% moisture content, respectively. The outlet stream of germ and fiber are produced at a rate of 4,228 and 3,871 kg/h, respectively. The germ is to be sold for extraction to produce corn crude oil. Hydrocyclone 1 Hydrocyclone 2 1st Grinding 1st_Screen 2nd_Screen ₃rd_Screen 1st_{Grind Tank} Corn Slurry Dewatering Screw Mixer Mixer Blending Tank Washing Water 2nd Grind Soaking Tank Corn Process Water

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Table 4.4. Operation information of germ and fiber recovery system (% (w/w) solids in the upflow).

Hydrocyclone system First wash Second wash Third wash

Ratio, 1st hydrocyclone (%) Ratio, 2nd hydrocyclone (%) Output (lb/bu) Ratio (%) Output (lb/bu) Ratio (%) Output (lb/bu) Ratio (%) Output (lb/bu) Ashes 42 25 0.361 58 0.289 60 0.207 73 0.151 Cellular Fiber 30 30 0.509 95 0.498 97 0.488 99 0.484 Coarse Fiber 83 85 2.454 95 2.401 97 2.352 99 2.330 Germ Fiber 73 75 0.784 95 0.767 97 0.751 99 0.745 Insoluble Germ Protein 83 82 0.451 95 0.441 97 0.432 99 0.428 Insoluble Nongerm Protein 30 11 1.378 50 1.206 40 0.689 25 0.172 Oil 85 86 2.062 95 2.017 97 1.976 99 1.958 Other 7 10 0.026 90 0.026 90 0.026 95 0.053 Soluble Protein 35 28 0.572 62 0.458 70 0.389 75 0.292 Starch 15 10 6.660 39 3.848 40 2.138 30 0.642 Sugars 31 35 0.740 50 0.618 40 0.331 37 0.122

After the germ and fiber separation, the starch concentration of the slurry increases to 27.9%. Because of the washing water input, the moisture content of the slurry improves to 66.7%, compared with 57% before the germ and fiber separation. The corn slurry is then fed into a fine grinder to get a desired particle size aiming at improving the performance of cooking hydration and subsequent enzymatic conversion to ethanol.

4.1.4 Liquefaction and Saccharification

The process of hydrolyzing starch to fermentable sugars (dextrins) uses a combination of heat and enzymes (Power, 2003). First, starch granules are hydrated in aqueous suspension, but they do not swell without heat. Therefore, a heater is set before the liquefaction tank to increase the slurry temperature to 88°C, providing the heat to separate the granules, rupture hydrogen bonds, and permit water to hydrate the starch molecule. However, this swelling process, called gelatinization, increases the viscosity of the mash, making it difficult to pump. Thus, before the gelatinization, Alpha-amylase is added at 0.082% dry basis of corn input (Kwiatkowski et al., 2006). Alpha-amylase is an enzyme that converts starch to dextrose, and thus reduces the viscosity of the solution. Lime and ammonia are also added in the tank to keep the slurry at pH

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6.5. The slurry is then mixed with ―backset‖, a recycled stream from the liquid portion of thin stillage. In addition to the water conservation, the backset provides critical nutrients for yeast propagation during fermentation and some heat to reduce the energy consumption. Liquefaction is initially held in the liquefaction tank (ID: 307 V) for 60 min at 88°C with agitation, and then cooked at 110°C for 15 min by using a jet cooker (Kwiatkowski et al., 2006). After the jet cooker heating, the slurry needs to be cooled down to meet the operational requirements of the following saccharification operation. To recover the extra heat of the outlet of the jet cooker, a heat

exchanger is designed to use preheat the slurry before going to the jet cooker. After the liquefaction step, starch has been broken down into dextrins as the reaction products.

Further conversion of dextrins to glucose is referred to saccharification. Glucoamylase is added at 0.11% (db) of corn to help to generate glucose (Kwiatkowski et al., 2006). To ensure a proper working environment for glucoamylase, a heat exchanger is set before the

saccharification tank to cool the slurry down to 60°C and sulfuric acid is added to lower the pH to 4.5. The slurry is held in the saccharification tank (ID: 318V) for 5 hr, and approximately 98% starch is converted into glucose (4.1). Glucoamylase continues to be active during fermentation if there are any unhydrolyzed dextrins remaining. Following the saccharification reaction, the stream is transferred to a heat exchanger with the heat being recovered by other streams in the process, and cooled by a cooler to 32°C prior to fermentation.

Starch + Water → Glucose (98% reaction efficiency) (4.1) 4.1.5 Fermentation

Fermentation is a conversion step where glucose is converted into ethanol and carbon dioxide with yeast. Urea is added at the rate of 0.3% (wet basis) of corn input to provide a nitrogen source for the yeast propagation. The fermentation (ID: 406 FR) simulated in this process is continuous, and the residence time is set at 60 hr. The very simple expression for fermentation shows that glucose yields almost the same amount of ethanol and carbon dioxide. In this model, 91.9% of total glucose is assumed to be converted to produce ethanol and carbon dioxide (4.2). Following Eq. (4.2), 3.28% of initial glucose is consumed for the yeast

propagation, and carbon dioxide is produced by the yeast metabolism (4.3). The remaining 4.82% glucose is not involved in the conversion and is left to the DDGS. The extent of

conversation is based on industrial data and research data, and the current ethanol concentration of the output is 15% (w/w). However, the reaction efficiency can be easily modified by users to

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meet their preferences. In addition, the chemical changes associated with fermentation of glucose to ethanol release heat energy of 515 Btu/lb of ethanol (Grethlein and Nelson, 1992). Ethanol coupled with high temperature is toxic to the yeast, which will affect the ethanol yield. Thus, cooling water is continuously provided to maintain the fermentation working temperature at 32°C.

Glucose → 0.511 Ethanol + 0.489 CO2 (91.9% reaction efficiency) (4.2)

0.88 Glucose + 0.12 Urea → 0.742 Yeast Dry Matter + 0.258 CO2 (40.5% reaction efficiency) (4.3)

98.5% of carbon dioxide and 2.25% of ethanol are emitted in the venting stream and transferred to the CO2 scrubber. To recover the emitted ethanol, fresh process water is fed in the

CO2 scrubber and is then recycled to the soaking tank. This is the only fresh process water input

in the system and is fed at a rate of 23,832 kg/h, which is 0.43 kg per kg of corn processed. The beer after the fermentation is preheated via two heat exchangers, recovering the heat from the inlet stream of saccharification tank and the outlet stream of the first distillation

column. The beer is then sent through a degasser, which is represented by a flash (ID: 408 V) in this model, to recover the residual carbon dioxide. Some ethanol and water are also purged to the vapor stream from the degasser. The vapor stream including CO2, ethanol, and water vapor are

processed to a condenser (ID: 409 C). The majority of ethanol and water in the vapor stream are condensed and then mixed with the liquid stream from the degasser for distillation.

4.1.6 Ethanol Recovery

The ethanol recovery sub-system considered here is designed to produce anhydrous ethanol from the beer for the use as fuel ethanol. As the azeotrope point of ethanol-water mixture is 95.6% of ethanol by volume, normal fractional distillation cannot produce anhydrous ethanol. In this analysis, the ethanol recovery sub-system consists of three distillation columns and a molecular sieve dehydrator that is used to accomplish the final dehydration step (Figure 4.3).

The complete normal fractional distillation system consists of a sequence of three columns, including a beer column (ID: 501 C) to pre-condense the ethanol concentration, a rectifier column (ID: 504 C) to concentrate the mixture close to the azeotrope point, and a stripping column (ID: 508 C) to minimize the ethanol loss. In the distillation system, reflux ratio is an important factor for estimating heating and cooling requirement as well as the number of stages in the column. The specific design parameters, such as reflux ratio (fraction of minimum

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reflux ratio (R / Rmin)), pressure, stage efficiency, and ethanol recovery ratio in the distillate, are

based on the USDA dry grind model (Kwiatkowski et al., 2006). Table 4.5 shows input data, specific design parameters, and output data for the design of this distillation sub-system.

Figure 4.3. The distillation sub-system in the QQ process.

Table 4.5. Specific design parameters and operating data of the distillation sub-system.

Beer Column Rectifier Column Stripping Column

R / Rmin 1.212 1.263 1.25

Pressure (bar) 1.03 1.03 1.03

Stage efficiency 36.4% 40% 40%

Ethanol recovery ratio 99.7% 99.44% 99%

Ethanol concentration in the feed (m/m)

14.99% 59.46% 0.92%

Ethanol concentration in the distillate (m/m)

60.82% 92.72% 7.42%

The first step in the ethanol recovery is the beer column, which captures 99.7% of the ethanol produced from the fermentation. The beer is fed to the column near the middle point whereas the steam is fed at the bottom. The volatile materials such as ethanol and water are recovered from the downward-flowing beer by the rising hot vapor. Some water vapor are then condensed and refluxed back to the column by the use of cooling water. After the beer column,

Beer Column Rectifier Column Stripping Column Molecular Sieve Mixer Beer Whole Stillage Ethanol